Simulation system for semiconductor process and simulation method thereof

ABSTRACT

Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

CROSS-REFERENCE TO THE RELATED APPLICATION(S)

This application is a continuation application of U.S. application Ser.No. 16/906,038, filed Jun. 19, 2020, now U.S. patent Ser. No. 11/574,095issued Feb. 7, 2023, which claims priority under 35 U.S.C. § 119 toKorean Patent Application No. 10-2019-0151830, filed on Nov. 25, 2019,in the Korean Intellectual Property Office, the disclosures of which areincorporated by reference herein in their entireties.

BACKGROUND

Example embodiments disclosed herein relate to a simulation device for asemiconductor device, and more particularly, relate to a simulationsystem for simulating a process of manufacturing a semiconductor deviceand a simulation method thereof.

A machine learning technique used in various technical fields mayimprove performance and efficiency in performing a task. The machinelearning technique may be applied to tune a semiconductor process, toreduce costs and provide high accuracy.

In designing the semiconductor process, a fine tuning of a process isneeded to obtain an optimum profile for equipment or conditions to beapplied to each process step. To tune a specific process (e.g., anetching process) including a plurality of steps (or process steps), aplurality of profiles corresponding to the plurality of steps arerequired. However, there is a limitation in checking the plurality ofprofiles to be applied to the respective plurality of process steps dueto cost and technology limitations.

In general, a process simulation may be used to obtain a profile of acompleted process. However, only a final profile is obtained through theprocess simulation, and a profile associated with an intermediate stepof a specific process may not be obtained. Accordingly, it is difficultto trace faults and problematic process steps of a process.

SUMMARY

One or more example embodiments provide a process simulation deviceusing a machine learning algorithm capable of estimating a profile foreach process step with high accuracy and a process simulation methodthereof.

According to an aspect of an example embodiment, there is provided asimulation method performed by a process simulator, the processsimulator being implemented with a recurrent neural network (RNN) drivenon a computer system and including a plurality of process emulationcells. The plurality of process emulation cells may be arranged in timeseries and configured to train and predict, based on a final targetprofile in a process of manufacturing a semiconductor, a profile of eachprocess step included in the process of manufacturing the semiconductor.The simulation method may include: receiving, at a first processemulation cell, a previous output profile provided at a previous processstep; receiving, at the first process emulation cell, a target profileof a current process step and process condition information indicatingone or more conditions to be applied in the current process step; andgenerating, at the first process emulation cell, a current outputprofile corresponding to the current process step, based on the targetprofile, the process condition information, and prior knowledgeinformation, the prior knowledge information defining a time seriescausal relationship between the previous process step and the currentprocess step.

According to an aspect of an example embodiment, there is provided aprocess emulation cell included in a recurrent neural network (RNN),driven on a computer system and including a plurality of processemulation cells. The plurality of process emulation cells may bearranged in time series and configured to train and predict, based on afinal target profile in a process of manufacturing a semiconductor, aprofile of each process step included in the process of manufacturingthe semiconductor. The process emulation cell may include: a profilenetwork configured to receive a previous output profile that is outputat a previous process step in time series, a target profile of a currentprocess step, and process condition information indicating one or moreconditions to be applied in the current process step, and configured togenerate a current output profile corresponding to the current processstep by performing a training operation, based on the target profile,the process condition information, and prior knowledge information; anda prior knowledge network configured to restrict the training of theprofile network based on the prior knowledge information provided froman outside. The prior knowledge information is provided to a function ora layer defining a time series causal relationship between the previousprocess step and the current process step in the process ofmanufacturing the semiconductor.

According to an aspect of an example embodiment, there is provided aprocess simulation system, which operates as a time series-basedrecurrent neural network (RNN) configured to receive a final targetprofile of a process of manufacturing a semiconductor, and configured totrain and predict a profile of each process step of the process ofmanufacturing the semiconductor. The process simulation system mayinclude: a random access memory (RAM) to which a process simulator isloaded; a central processing unit configured to execute the processsimulator to perform training using the recurrent neural network; and aninput/output interface configured to perform at least one of: receivingat least one of an input to the process simulator or the final targetprofile and transfer the at least one of the input or the final targetprofile to the central processing unit, or outputting a profilegenerated based on the training, the profile corresponding to a givenprocess step of the process manufacturing the semiconductor, wherein theprofile is generated based on the training, which is performed based onprior knowledge information defining a time series causal relationshipin the process of manufacturing the semiconductor.

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects, features, and advantages of certain exampleembodiments will be more apparent from the following description takenin conjunction with the accompanying drawing.

FIG. 1 is a block diagram illustrating a process simulation systemaccording to an example embodiment.

FIGS. 2A to 2C are diagrams for explaining a process simulator of anexample embodiment.

FIGS. 3A and 3B are diagrams illustrating a schematic characteristic ofone of process emulation cells illustrated in FIG. 2A.

FIG. 4 is a table schematically illustrating information or parametersprovided to train a process emulation cell according to an exampleembodiment.

FIG. 5 is a diagram for explaining an example computation structure of aprofile network illustrated in FIG. 3 .

FIG. 6 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment.

FIG. 7 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment.

FIGS. 8A to 8C are diagrams illustrating a configuration and acharacteristic of a process emulation cell according to an exampleembodiment.

FIG. 9 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment.

FIG. 10 is a diagram schematically illustrating an example of priorinformation provided from another simulation software according to anexample embodiment.

FIG. 11 is a diagram illustrating a configuration and a characteristicof a process emulation cell according to another example embodiment.

FIG. 12 is a diagram illustrating a configuration and a characteristicof a process emulation cell according to another example embodiment.

FIG. 13 illustrates a configuration of a process emulation cellaccording to an example embodiment.

FIG. 14 is a diagram illustrating a configuration and a characteristicof a process emulation cell according to another example embodiment.

DETAILED DESCRIPTION

It should be understood that both the foregoing general description andthe following detailed description are provided as examples, and itshould be regarded as an additional description is provided. Referencenumerals will be represented in detail in example embodiments, examplesof which are illustrated in the accompanying drawings. Whereverpossible, the same reference numerals are used in the drawings and thedescription to refer to the same or similar parts.

Below, example embodiments of the disclosure may be described in detailand clearly to such an extent that an ordinary skilled in the art caneasily implement the disclosure.

FIG. 1 is a block diagram illustrating a process simulation systemaccording to an example embodiment.

Referring to FIG. 1 , a process simulation system 100 according to anexample embodiment may include a central processing unit (CPU) 110, arandom access memory (RAM) 120, an input/output interface 130, storage140, and a system bus 150. Here, the process simulation system 100 maybe implemented with a dedicated device for performing the processsimulation by using machine learning according to an example embodiment,but may be implemented with a computer or a workstation driving a designprogram such as a TCAD (Technology Computer-Aided Design) or ECAD(Electronic Computer-Aided Design) simulation program.

The CPU 110 runs software (e.g., an application program, an operatingsystem, and device drivers) to be executed in the process simulationsystem 100. The CPU 110 may execute the operating system OS (notillustrated) loaded to the RAM 120. The CPU 110 may execute variousapplication programs to be driven based on the operating system OS. Forexample, the CPU 110 may execute a process simulator 125 loaded to theRAM 120. The process simulator 125 of an example embodiment may includea machine learning (ML) algorithm that uses given training data.

The operating system OS or the application programs may be loaded to theRAM 120. When the process simulation system 100 is booted up, an OSimage (not illustrated) stored in the storage 140 may be loaded to theRAM 120 depending on a booting sequence. Overall input/output operationsof the process simulation system 100 may be supported by the operatingsystem OS. Additionally, the application programs that are selected by auser or are used to provide a basic service may be loaded to the RAM120. In particular, the process simulator 125 of an example embodimentmay also be loaded from the storage 140 to the RAM 120. The RAM 120 maybe a volatile memory, such as a static random access memory (SRAM) or adynamic random access memory (DRAM), or a nonvolatile memory, such as aphase-change RAM (PRAM), a magnetic RAM (MRAM), a resistive RAM (ReRAM),a ferroelectric RAM (FRAM), or a NOR flash memory.

The process simulator 125 performs a process simulation operation byusing the machine learning algorithm according to an example embodiment.In particular, the process simulator 125 may be trained in a manner inwhich a feature of a time series profile for each process operation isreflected. For example, in the case of applying a general machinelearning technique to simulate an etching process, an irreversible timeseries profile may be generated. There may be a case in which, inapplying the general machine learning technique, a profile of anintermediate process step is difficult to generate physically orexperientially. However, the process simulator 125 of an exampleembodiment may use functions or data capable of solving the above issuein various schemes. This will be described in detail later withreference to drawings.

The input/output interface 130 controls user inputs and outputs from andto user interface devices. For example, the input/output interface 130may include a keyboard or a monitor and may be provided with a commandor data from the user. Also, target data to be used for training theprocess simulator 125 of an example embodiment may be provided throughthe input/output interface 130. The input/output interface 130 maydisplay a progress and a processed result of each simulation operationof the process simulation system 100.

The storage 140 is provided as a storage medium of the processsimulation system 100. The storage 140 may store the applicationprograms, the OS image, and various kinds of data. In addition, thestorage 140 may store and update trained data 144 as the processsimulator 125 is driven. The storage 140 may be implemented with amemory card (e.g., a multimedia card (MMC), an embedded MMC (eMMC), asecure digital (SD), and a microSD) or a hard disk drive (HDD). Thestorage 140 may include a high-capacity NAND-type flash memory.Alternatively, the storage 140 may include a next-generation nonvolatilememory, such as a PRAM, an MRAM, a ReRAM, or a FRAM, or a NOR flashmemory.

The system bus 150 is a system bus for providing a network within theprocess simulation system 100. The CPU 110, the RAM 120, theinput/output interface 130, and the storage 140 may be electricallyconnected through the system bus 150 and may exchange data with eachother through the system bus 150. However, the configuration of thesystem bus 150 is not limited to the above description and may furtherinclude arbitration devices for efficient management.

According to the above description, the process simulation system 100may perform simulation on a semiconductor manufacturing process by usingthe process simulator 125. When a final profile is provided, profilescorresponding to intermediate process steps may also be provided by theprocess simulation system 100. A time series causal relationship may bereflected to the profiles corresponding to the intermediate processsteps by the process simulator 125 of an example embodiment.

FIGS. 2A to 2C are diagrams for explaining a process simulator of anexample embodiment. FIG. 2A illustrates an example structure of theprocess simulator 125 of an example embodiment, which is implementedwith a recurrent neural network RNN. FIG. 2B illustrates an operation ofestimating profiles Y1, Y2, . . . Yk at intermediate process steps basedon a final profile Yn. FIG. 2C is a diagram illustrating removal of anerror of machine learning by the process simulator 125 of an exampleembodiment.

Referring to FIG. 2A, the process simulator 125 of an example embodimentmay include the recurrent neural network RNN in which process emulationcells 122, 124, and 126 corresponding to respective process steps arearranged in time series. The recurrent neural network RNN isadvantageous to process data sequentially in time. That is, an output ofone layer may be used as an input of another layer. The processsimulator 125 may represent a process such as, for example but notlimited to, etching, deposition, diffusion, and implantation, as aprocess including a plurality of process steps progressing in timeseries. Each process step means a process unit in which there is achange to at least one of conditions, which are applied to a process,such as a temperature, a gas kind, a pressure, and an exposure time, orthere is a change to equipment that is used. For the convenience ofexplanation, the process simulator 125 that applies to a process ofetching is described below as an example.

First, the process emulation cell 122 corresponding to a first processstep outputs an output Y1 (or a current output profile) corresponding toa profile that is obtained at a current process step by using anexecution result Y₀ (or a previous output profile) of a previous processstep and an input X₁ of the current process step. For example, theprocess emulation cell 122 is a layer of the recurrent neural networkRNN, which emulates the first step of the etching process. Accordingly,the execution result Y₀ of the previous process step, which is providedto the first step of the etching process, is an initial value (e.g., aprofile of a state in which etching is not made). In addition, the inputX₁ of the current process step may include parameters associated withconditions to be applied to the first process step, which is the firststep of the etching process. Each of the process emulation cells 122,124, and 126 includes a profile network PN configured to performtraining of the recurrent neural network RNN that receives an output ofa previous process step and predicts a next process step. In addition,each of the process emulation cells 122, 124, and 126 of an exampleembodiment includes a prior knowledge network PKN for minimizing anerror due to the freedom of neural network training within each processemulation cell.

The process emulation cell 124 uses the execution result Y1 of the firstprocess step and an input X₂ of a second process step for the purpose ofsimulating the second process step. The input X₂ of the second processstep may include parameters associated with conditions to be applied tothe second process step, which is the second step of the etchingprocess. The structure of the process emulation cell 124 issubstantially identical to the process emulation cell 122. That is, theprocess emulation cell 124 also includes the profile network PN forperforming training of the recurrent neural network RNN and the priorknowledge network PKN for removing an error of a profile. An input X_(i)of each process step associated with each process condition is variable,but the process emulation cells 122, 124, and 126 may each include theprofile network PN and the prior knowledge network PKN have the samestructure.

The process emulation cell 126 performs a recurrent neural networkoperation corresponding to the last process step. The process emulationcell 126 may perform prediction and training on a profile of the lastprocess step by using an output Yn−1 of a previous process step and aninput Xn of an n-th process step (i.e., the last process step).

In the case where the training is sufficiently performed on the lastoutput Y_(n) corresponding to the target profile (or the final targetprofile), profiles corresponding to the outputs Y1 to Yn−1 of therespective process steps may be profiles of high accuracy. That is, theprofiles corresponding to the outputs Y1 to Yn−1 of the respectiveprocess steps may be accurately estimated to be substantially the sameas profiles that are actually generated in the respective process steps.

FIG. 2B is a diagram illustrating a characteristic of the processsimulator 125 of an example embodiment illustrated in FIG. 2A. Referringto FIG. 2B, the outputs Y1, Y2, and Yk of intermediate process stepsaccording to the shape of the last output Yn corresponding to the finalprofile of an example embodiment are illustrated as an example.

In the last output Yn, a hole having a depth Dn may be formed in asubstrate SUB as illustrated in FIG. 2B. The last output Y_(n) may be,for example, in a shape of a target profile that is output from theprocess simulator 125. In the case where a profile of the same shape asthe last output Y_(n) is provided, the process simulator 125 accordingto an example embodiment may calculate outputs of intermediate processsteps in a shape of a profile that does not contradict a physical shapeformed in an actual etching process or an experiential shape.

For example, in the output Y1 of the first process step, a depth D1 ofan etched hole produced through simulation may always have a smallervalue than a depth D2 of a hole corresponding to the output Y2 of thesecond process step. However, when the machine learning using a generalrecurrent neural network RNN is applied, this rule may be violated. Incontrast, according to the process simulator 125 of an exampleembodiment, a profile of a shape that cannot be generated experientiallyor due to a physical law in time series may be prevented. That is, adepth DK of an etched hole corresponding to the output Yk of a k-thprocess step has a value that is at least equal to or greater than adepth of an etched hole of a previous process step. According to anexample embodiment, it may be possible to obtain a more accuracy profileof an intermediate process step through the process simulator 125 thatuses information about the time series causal relationship.

The aspects and advantages of an example embodiment are described abovewith reference to an etching process, but the disclosure is not limitedthereto. For example, in the implantation process or the depositionprocess, the process simulator 125 may be implemented and trained in thesame manner as described above, except that parameters and a shape of afinal profile change according to the type of the process.

FIG. 2C is a diagram schematically illustrating an advantage provided bya process simulator of an example embodiment. Referring to FIG. 2C, acase that contradicts a time series causal relationship may be detectedby the process simulator 125 according to an example embodiment. Forexample, a profile Ym−1 of a (m−1)-th process step preceding in timeseries and a profile Ym of an m-th process step are schematicallyillustrated.

When a simulation is performed using the general machine learning, theprofile Ym−1 marked by a dotted line may show that etching is furthermade, compared to the profile Ym marked by a solid line. That is, aprofile of a process step that is first performed in time series in anetching process may show that etching is made more excessively than aprofile of a process step later performed in the etching process. Thiserror may result from the training that is based on limited informationof an intermediate process step of the machine learning. The priorknowledge network PKN that is used in the process simulator 125 of anexample embodiment may prevent the generation of a profile that cannotbe produced in an actual process or contradicts the physical law.Therefore, the process simulator 125 of an example embodiment mayestimate a profile of each process step in a process with high accuracy.Also, based on the estimated profile for each process step according toan example embodiment, a defect inspection operation of detecting adefect in a process step may be performed with high accuracy.

FIGS. 3A and 3B are diagrams illustrating a schematic characteristic ofone of process emulation cells illustrated in FIG. 2A. Referring to FIG.3A, a process emulation cell 200 that corresponds to one basic unit forconstituting the recurrent neural network RNN includes a profile network210 and a prior knowledge network 230.

The profile network 210 performs training for generating an output Yk ofa current process step “k” (k being a natural number and 1≥k≥n) by usinginputs Xk and Xcom provided at the current process step and an outputYk−1 of a previous process step “k−1”. For example, the profile network210 performs training by using a target profile of a current processstep “k” and the output Yk−1 of the previous process step “k−1”. Thefirst input Xk includes coordinates or various process conditionscorresponding to the target profile of the current process step “k”. Thesecond input Xcom may include a value defining recipes to be applied incommon to all process steps including the current process step “k”.

The prior knowledge network 230 may control a training operation of theprofile network 210. For example, the prior knowledge network 230provides a filtering value or a limit value of the output Yk of thecurrent process step “k” trained by the profile network 210. In the casewhere simulation is performed on an etching process, the prior knowledgenetwork 230 may filter a profile of a shape that cannot be generated atthe output Yk of the current process step “k” with reference to theetching amount of each step of the etching process. Alternatively, theprior knowledge network 230 may restrict a profile of a shape thatcannot be generated at the output Yk of the current process step “k” byusing a cumulative etching amount of process steps progressing in timeseries. To perform this filtering, the prior knowledge network 230 mayutilize at least one of various prior knowledges based upon which theoutput Yk of the current process step is forced. A shape of an outputmay be forced by newly defining a loss function by using a priorknowledge, by restricting a boundary of an output value, or by using anexperiential profile. The prior knowledge may also include a resultvalue of another simulation software (e.g., TCAD).

Referring to FIG. 3B, a training and profile generation operation thatis performed by using prior knowledge information at the processemulation cell 200 of FIG. 3A is illustrated.

In operation S110, the process emulation cell 200 receives the outputYk−1 of the previous process step “k−1”. When the current process stepis the first process step, the previous process step may not exist, andthus, the initial value Y0 may be provided as the output of the previousprocess step “k−1”.

In operation S120, the process emulation cell 200 receives the inputs Xkand Xcom provided at the current process step “k” (k being a naturalnumber and 1≥k≥n). The inputs Xk and Xcom include information defining ashape of a target profile X_(WL) of a current process step and processcondition information. Here, operation S110 and operation S120 may besimultaneously performed, or one of operation S110 and operation S120may be first performed.

In operation S130, the process emulation cell 200 may perform trainingby using the profile network 210 and the prior knowledge network 230 andmay generate the output Yk of the current process step “k”. Inparticular, the prior knowledge network 230 may restrict or guide thetraining operation of the profile network 210. The generation of aprofile that is impossible in a process progressing in time series maybe prevented by the prior knowledge network 230.

In operation S140, the process emulation cell 200 may transfer theoutput Yk of the current process step “k” to a next process emulationcell linked in time series. However, in the case where the output Yk ofthe current process step “k” is the last process step of the processsimulator 125, the process emulation cell 200 may output the output Ykof the current process step as a final result value Yn.

An operation that is performed at the process emulation cell 200 of anexample embodiment is briefly described above. However, it may be wellunderstood that the function or configuration of the prior knowledgenetwork 230 for restricting the training operation or the predictionoperation of the process emulation cell 200 may be variously changedwithout limitation to the above description.

FIG. 4 is a table schematically illustrating information or parametersprovided to train a process emulation cell according to an exampleembodiment. Input data of the table illustrated in FIG. 4 may beprovided to the process emulation cell 200. Inputs that are provided tothe process emulation cell 200 may be classified into four groups:{circle around (1)}, {circle around (2)}, {circle around (3)}, and{circle around (4)}.

Coordinates corresponding to a target profile X_(WL) may be included inthe input of the first group {circle around (1)}. The target profileX_(WL) that is intended to be formed through a process step exists foreach process step. In the case of an etching process, shapes of targetprofiles of respective process steps are differently defined. Forexample, a depth “D” and a width “W” of an etched hole may be defined toincrease in every process step. The target profile X_(WL) may beexpressed by the coordinates and data of the coordinates are processedby the process emulation cell 200.

The output Yk−1 of the previous process step (“Incoming Structure”) andmold information Xcom are included in the second group a of input data.The output Yk−1 of the previous process step may include a value that isessential to the RNN operation that performs time series processing. Theoutput Yk−1 of the previous process step is a value corresponding to ashape of a profile trained at the previous process step. The moldinformation Xcom is information about a material of a substrate or amask under an etching, deposition, or implantation process. Informationabout a thickness of a substrate or a mask or information about a kindof a material of the substrate or mask may be included in the moldinformation Xcom.

Information about equipment or a chamber for a process being currentlyapplied or information about preventive maintenance (“PM information”)may be included in the third group {circle around (3)} of input data.The information included in the third group {circle around (3)} may varydepending on the equipment and the chamber and may be commonly providedto all of process steps together with the mold information.

Information about a reticle to be used in a process being currentlyapplied, information about at least one process performed before theprocess being currently applied (“Structure Scheme”), constantparameters to be applied to the current process (“Constant Recipe”), anda sequence recipe Xk of the current process step may be included in thefourth group {circle around (4)} of input data. Except for the sequencerecipe Xk, the inputs Xcom that are applied in common to all of processsteps are included in the input data of the fourth group {circle around(4)}. In contrast, the sequence recipe Xk may include the informationabout conditions applied in a current step of a process to be simulated.Examples of the sequence recipe Xk may include a kind of a gas, aconcentration, a temperature, a pressure, and an application time.

Examples of inputs to be provided to the process emulation cell 200 aredescribed as schematically being classified into four groups {circlearound (1)}, {circle around (2)}, {circle around (3)}, and {circlearound (4)}, but it should be understood that an example embodiment isnot limited to the above example. Additional information may be furtherincluded, or one or more of the above inputs may be selectively omitteddepending on an embodiment.

FIG. 5 is a diagram for explaining an example computation structure of aprofile network illustrated in FIG. 3 . Referring to FIG. 5 , theprofile network 210 includes function blocks constituting the generalrecurrent neural network RNN.

First, the first and second group inputs {circle around (1)} and {circlearound (2)} are concatenated by a first concatenation element 211. Thatis, an input value of the second group a including the output Yk−1 ofthe previous process step and the mold information Xcom and thecoordinates corresponding to a target profile X_(WL) are concatenated bythe first concatenation element 211.

A concatenated value of the first concatenation element 211 is processedby a first training neural network 212. The first training neuralnetwork 212 may include a convolutional neural network CNN using thefirst and second group inputs {circle around (1)} and {circle around(2)}. Data that are output from the convolutional neural network CNN maybe data that are multi-dimensionally arranged. A flattening element 213performs a task to one-dimensionally rearrange the multi-dimensionallyarranged output of the first training neural network 212.

An embedding element 214 converts the third group input {circle around(3)} to a value capable of being processed by the profile network 210.Information about equipment or a chamber or setting information aboutthe equipment or chamber may be information of a category form.Accordingly, the embedding element 214 performs embedding processing toconvert this category information into numerical information. At leastone of schemes, which are utilized in the recurrent neural network RNN,such as skip-gram, negative sampling, and GloVe may be applied to theembedding processing.

Data flattened by the flattening element 213, an output of the embeddingelement 214, and the fourth group input {circle around (4)} may beconcatenated by a second concatenation element 215. The dataconcatenated by the second concatenation element 215 are trained by adeep neural network 216. The output Yk of the current process step maybe provided as the trained result.

FIG. 6 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment. Referring to FIG. 6 , a processemulation cell 200 a according to an example embodiment may prevent anabnormal profile from being generated, through an enhanced loss function230 a included in a profile network 210 a.

The enhanced loss function (L_(k)) 230 a is applied to the profilenetwork 210 a every process step. The profile network 210 a of anexample embodiment may receive the output Yk−1 of the previous processstep, the common input Xcom, and the sequence recipe Xk of the currentprocess step. The training or prediction operation may be performed byusing the input values in the scheme described with reference to FIG. 5. In particular, the enhanced loss function (Lk) 230 a is used in theprofile network 210 a. The training may be performed to suppress thegeneration of a profile of a shape that is impossible to appear in anactually physical environment through the enhanced loss function (L_(k))230 a.

In general, a loss function is used as an index indicating a trainingstate of the recurrent neural network RNN. The profile network 210 a mayrepeat the training procedure of adjusting a weight parameter forreducing the size of the enhanced loss function (L_(k)) 230 a. Inparticular, the enhanced loss function (L_(k)) 230 a may be configuredsuch that a greater loss function is generated in the case of a profileof a shape that cannot be allowed at a previous process step and acurrent process step. For example, the enhanced loss function (L_(k))230 a may be defined by Equation 1 below.

$\begin{matrix}{L_{k} = {L + {\lambda{\sum\limits_{k = 1}^{n}{{Relu}\left( {Y_{k - 1} - Y_{k}} \right)}}}}} & \left\lbrack {{Equation}1} \right\rbrack\end{matrix}$

Here, “L” may use a mean squared error (MSE) or a cross entropy error(CEE) defined in a conventional loss function. In particular, in anexample embodiment, a “Relu” function for a difference value “Yk−1−Yk”between an output value of a previous process step and an output valueof a current process step is added to the loss function. The “Relu”function is a function in which an output value of “0” is output withregard to an input value smaller than “0” and an output value linearlyincreases with regard to increase of an input value of “0” or more.Accordingly, when the difference value “Yk−1−Yk” obtained by subtractingan output value Yk of a current process step from an output value Yk−1of a previous process step is greater than “0” and increases, a value ofa loss function increases. Accordingly, the training may be made toreduce a value of the enhanced loss function. In addition, a value ofthe “Relu” function accumulated from the first process step to thecurrent process step “k” is applied to a loss function L_(k) of thecurrent process step “k”. As a weight “k” is applied, the strength ofthe tendency of training may be controlled. In the case of applying theloss function L_(k) described with reference to Equation 1, the trainingmay be made to suppress the case where a profile of a previous processstep has a greater value than a profile of a current process step.Accordingly, the loss function L_(k) of Equation 1 may be applied toprocesses, in which a process result at a current process step alwaysincreases compared to a previous process step, such as etching,deposition, and implantation. In contrast, the loss function L_(k)expressed by Equation 2 below may be used in a process, in which a totalprocess amount is always preserved, such as diffusion. The enhanced lossfunction (L_(k)) 230 a may be defined by Equation 2 below.

$\begin{matrix}{L_{k} = {L + {\lambda{\sum\limits_{k = 1}^{n}\left( {{\int Y_{k - 1}} - {\int Y_{k}}} \right)^{2}}}}} & \left\lbrack {{Equation}2} \right\rbrack\end{matrix}$

Here, “L” and “k” may denote the same parameters as Equation 1. A dopantof the same amount has to be implanted at all steps of the diffusionprocess. Accordingly, the loss function L_(k) for the diffusion processmay be implemented to suppress the case where a total amount of a dopantof a previous process step and a total amount of a dopant of a currentprocess step is variable. Accordingly, a value that corresponds to asquare of a value obtained by subtracting the dopant amount of thecurrent process step from the dopant amount of the previous process stepmay be reflected to a loss function.

The loss function L_(k) expressed by Equation 1 or Equation 2 is onlygiven as an example and the enhanced loss function (L_(k)) 230 aaccording to an example embodiment is not disclosed thereto.

The process simulator 125 (refer to FIG. 1 ) may be trained by theprocess emulation cell 200 a for each process step using the aboveenhanced loss function (L_(k)) 230 a so as to generate a profile shapethat may be formed in an actual process.

FIG. 7 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment. Referring to FIG. 7 , a processemulation cell 200 b according to an example embodiment may include aprofile network 210 b that provides a process result Y*k generated at acurrent process step and a custom layer 230 b that defines a causalrelationship between a previous process step and a current process step.

The profile network 210 b may receive the output Yk−1 of the previousprocess step, the common input Xcom, and the sequence recipe Xk of thecurrent process step. The training or estimation operation may beperformed by using the input values in the scheme described withreference to FIG. 5 . In particular, the profile network 210 b may beconfigured to infer and train the process result Y*k of the currentprocess step by using the above input parameters, instead of generatingthe accumulated process result Yk. That is, the profile network 210 bmay output and train the process result Y*k corresponding to a processamount of only the current process step, not a profile corresponding toa total accumulated process amount accumulated by the first process stepto the current process step.

The custom layer 230 b receive the output Yk−1 of the previous processstep and the process result Y*k of the current process step. The customlayer 230 b is an added layer to calculate a causal relationship inwhich a process result Y*k of a current process step is accumulated onthe output Yk−1 corresponding to a process result of a previous processstep. For example, in the case of the etching process, the custom layer230 b deduces a physical law that an etching amount of a current processstep is added to the output Yk−1 corresponding to a process result of aprevious process step, resulting in the accumulated processing result Ykin the current process step. The physical law applied in each processstep may be applied by the custom layers 230 b included in the processemulation cells in each of process steps.

An operation in which the custom layer 230 b receives and processes theoutput Yk−1 of the previous process step and the process result Y*k ofthe current process step may be expressed by Equation 3 below.Y _(k) =Y _(k-1) +Y _(k)*  [Equation 3]

According to Equation 3 above, it would be understood that the output Ykof the current process step generated at the profile network 210 b isobtained by adding the process result Y*k generated at the currentprocess step to the output Yk−1 of the previous process step. That is,the above custom layer 230 b may be applied to a process such asetching, deposition, or implantation. However, this physical law may notbe applied to a diffusion process in which a total process amount ispreserved. Accordingly, it may be difficult to apply the modeling fordiffusion to the process simulation using the custom layer 230 b.

FIGS. 8A to 8C are diagrams illustrating a configuration and a featureof a process emulation cell according to an example embodiment.Referring to FIG. 8A, a process emulation cell 200 c according to anexample embodiment may include a profile network 210 c that trains andgenerates critical parameters (CP) (λ, z) and an activation functionlayer 230 c that applies an activation function. Here, the activationfunction layer 230 c performs a function of the prior knowledge network230 (refer to FIG. 3 ).

The profile network 210 c may receive the output Yk−1 of the previousprocess step, the common input Xcom, and the sequence recipe Xk of thecurrent process step. The training or estimation operation may beperformed by using the input values in the scheme described withreference to FIG. 5 . In particular, the profile network 210 c trainsand generates the critical parameters (λ, z) by using the output Yk−1 ofthe previous process step, the common input Xcom, and the sequencerecipe Xk of the current process step. The critical parameters (λ, z)include variables that determine a form of an activation function.

The activation function layer 230 c is provided with the trained orestimated critical parameters (λ, z) from the profile network 210 c. Inaddition, the activation function layer 230 c is provided with aphysical parameter (PP) “x” of the current process step. The physicalparameter “x” may mean, for example, a variable indicating a size of adepth or a width in an etching process. An activation function thatforces a shape of a profile of each process step to be similar to anactual physical shape is used in the activation function layer 230 c. Inthe case of the etching process, an activation function may have a shapeof a function indicating an etching amount of a current process step oran etching amount accumulated from the first process step to the currentprocess step. The activation function that is in the shape of anexponential function modeling the etching amount of the current processstep may be modeled as Equation 4 below.

$\begin{matrix}{Y_{k}^{*} = {{f(x)} = {\frac{z}{\lambda}\left( \frac{x}{\lambda} \right)^{z - 1}e^{- {(\frac{x}{\lambda})}^{z}}}}} & \left\lbrack {{Equation}4} \right\rbrack\end{matrix}$

Here, “x” indicates a physical parameter (e.g., an etch depth), “λ” and“z” indicate critical parameters. An activation function f(x) expressedby Equation 4 models an etch amount in each process step in the form ofan exponential function.

However, the activation function may be a function indicating an etchamount accumulated from the first process step to the current processstep. In this case, an output of the activation function layer 230 c maybe the output Yk indicating a profile accumulated until the currentprocess step. In the above condition, the profile network 210 c may betrained to generate the critical parameters (λ, z) for generating theaccumulated output Yk or the output Y*k of the current process step inthe same shape as the activation function.

FIG. 8B is a graph illustrating an activation function expressed byEquation 4. Referring to FIG. 8B, an activation function indicating anetch amount of each of three process steps 1, 3, and 8 is illustrated.

For example, it may be understood that an activation function Y*1 of thefirst process step (k=1) has a relatively great etch amount at a shallowdepth “x”. In addition, an activation function of the eighth processstep (k=8) is an exponentially increasing function indicating a maximumetch amount at a relatively deep depth “x”. The profile network 210 cmay train the critical parameters (λ, z) such that the output Y*k of thecurrent process step is generated in accordance with the shapes of theabove activation functions.

FIG. 8C is a graph illustrating a shape of an activation function oncoordinates, which has a shape indicating an etch amount accumulatedfrom a first process step to a current process step. Referring to FIG.8C, an activation function indicating an accumulated etch amount of eachof four process steps 3, 5, 7, and 9 is illustrated.

It may be understood from an activation function Y5 of the fifth processstep (k=5) that an etch amount in a direction of a depth “x” of asubstrate is further increased compared to an activation function Y3 ofthe third process step (k=3). An activation function may be decided byan etch amount accumulated along the depth “x” of the substrate atrespective process steps. In this case, the profile network 210 c maytrain the critical parameters (λ, z) such that the output Y*k of thecurrent process step is generated in the shape of an activation functioncorresponding to the accumulated etch amount.

FIG. 9 is a diagram illustrating a configuration of a process emulationcell according to an example embodiment. Referring to FIG. 9 , a processemulation cell 200 d according to an example embodiment may include aprofile network 210 d that performs training and estimation by usingprior information Y′k obtained through another simulation program (e.g.,TCAD 300). Here, the TCAD 300 is mentioned as an example of anothersimulation program, but the disclosure is not limited thereto. The priorinformation Y′k corresponding to a schematic profile for training andestimating the output Yk of the current process step may be obtained invarious modeling manners or simulation software.

An enhanced loss function (L_(k)) 230 d is applied to the profilenetwork 210 d of each step of a process. The profile network 210 d of anexample embodiment may receive the output Yk−1 of the previous processstep, the common input Xcom, and the sequence recipe Xk of the currentprocess step. The profile network 210 d may perform a training orprediction operation by using the input values in the scheme describedwith reference to FIG. 5 .

In the example embodiment illustrated in FIG. 9 , the enhanced lossfunction (L_(k)) 230 d using the prior information Y′k provided from theTCAD 300 is used in the profile network 210 d. The profile network 210 dmay perform training through the enhanced loss function (L_(k)) 230 dsuch that the output Yk is converged to be similar in shape to the priorinformation Y′k. The profile network 210 d may repeat the trainingprocedure of manipulating a weight or a parameter for reducing the sizeof the enhanced loss function (L_(k)) 230 d. For example, the enhancedloss function (L_(k)) 230 d may be expressed by Equation 5 below.

$\begin{matrix}{L_{k} = {L + {\lambda{\sum\limits_{k = 1}^{n}\left( {Y_{k}^{\prime} - Y_{k}} \right)^{2}}}}} & \left\lbrack {{Equation}5} \right\rbrack\end{matrix}$

Here, “L” may use a mean squared error (MSE) or a cross entropy error(CEE) defined in a conventional loss function. “λ” is a weight, and theprior information Y′k is prior information provided from the TCAD 300.The profile network 210 d may perform training such that the size of theenhanced loss function (L_(k)) 230 d is reduced. Accordingly, the outputYk may be trained by the profile network 210 d using the enhanced lossfunction L_(k) of Equation 5, so as to be similar or identical to theprior information Y′k in shape.

In the case where the prior information Y′k provided by the simulationsoftware such as the TCAD 300 has high accuracy, the effect of trainingmay be improved through Equation 5 above. However, in some cases, theprior information Y′k may provide a schematic shape of each process stepor a progress tendency of a process, rather than providing an accurateshape of a profile. In this case, the enhanced loss function L_(k)expressed by Equation 6 below may be used.

$\begin{matrix}{L_{k} = {L + {\lambda{\sum\limits_{k = 1}^{n}{\sum\limits_{l = 1}^{m}\left( {\frac{\partial Y_{k}^{\prime}}{\partial Z^{l}} - \frac{\partial Y_{k}}{\partial Z^{l}}} \right)^{2}}}}}} & \left\lbrack {{Equation}6} \right\rbrack\end{matrix}$

By the process emulation cell 200 d of each process step using theenhanced loss function (L_(k)) 230 d, the process simulator 125 (referto FIG. 1 ) may be trained to output a profile shape implemented byanother simulation software. In this case, the training speed of theprocess simulator 125 may be substantially improved.

FIG. 10 is a diagram schematically illustrating prior informationprovided from another simulation software. FIG. 10 shows a graphschematically illustrating the prior information Y′k corresponding toprofiles of respective process steps and provided from the TCAD 300. Itmay be understood from FIG. 10 that a schematic shape is similar to ashape of an actual profile even if the schematic shape is somewhatdifferent from an actually physical profile shape. In addition, even ifthe prior information Y′k does not provide an accurate shape of aprofile, the prior information Y′k may provide the development ofchanges for each process step. In this case, a curve that the priorinformation Y′k indicates may be somewhat shaped by differentiating thecurve.

FIG. 11 is a diagram illustrating a configuration and a characteristicof a process emulation cell according to another example embodiment.Referring to FIG. 11 , a process emulation cell 200 e provides astructure based on a combination of the example embodiment of FIG. 6 andthe example embodiment of FIG. 8A.

An enhanced loss function (L_(k)) 230 e is applied to a profile network210 e. The profile network 210 e of an example embodiment may receivethe output Yk−1 of the previous process step, the common input Xcom, andthe sequence recipe Xk of the current process step. The training orprediction operation may be performed by using the input values. Inparticular, the enhanced loss function (L_(k)) 230 e is used in theprofile network 210 e. Training may be performed based on the enhancedloss function (L_(k)) 230 e such that generation of a profile of whichof a shape cannot appear in a real application is suppressed. Theprofile network 210 e of an example embodiment generates and trains thecritical parameters (λ, z) that are provided to an activation functionlayer 234 e.

The activation function layer 234 e is provided with the trained orestimated critical parameters (λ, z) from the profile network 210 e. Inaddition, the activation function layer 234 e is provided with thephysical parameter “x” of the current process step. The physicalparameter “x” may mean, for example, a variable indicating a size of adepth or a width in an etching process. An activation function thatforces a shape of a profile of each process step to be similar to anactual physical shape is used in the activation function layer 234 e.However, the activation function may be in the shape of a functionindicating an etch amount accumulated from the first process step to thecurrent process step. In this case, an output of the activation functionlayer 234 e may be the output Yk indicating a profile accumulated untilthe current process step. In the above condition, the profile network210 e may be trained to generate the critical parameters (λ, z) forgenerating the accumulated output Yk or the output Y*k of the currentprocess step in the same form as the activation function.

FIG. 12 is a diagram illustrating a configuration and a characteristicof a process emulation cell according to another example embodiment.Referring to FIG. 12 , a process emulation cell 200 f provides astructure based on a combination of the example embodiment of FIG. 7 andthe example embodiment of FIG. 8A. The process emulation cell 200 f mayinclude a profile network 210 f, an activation function layer 232 f, anda custom layer 234 f.

The profile network 210 f may receive the output Yk−1 of the previousprocess step, the common input Xcom, and the sequence recipe Xk of thecurrent process step. The training or prediction operation may beperformed by using the input values in the scheme described withreference to FIG. 5 . In particular, the profile network 210 f trainsand generates the critical parameters (λ, z) by using the output Yk−1 ofthe previous process step, the common input Xcom, and the sequencerecipe Xk of the current process step.

The activation function layer 232 f is provided with the trained orestimated critical parameters (λ, z) from the profile network 210 f. Inaddition, the activation function layer 232 f is provided with thephysical parameter “x” of the current process step. The physicalparameter “x” may mean, for example, a variable indicating a size of adepth or a width in an etching process. An activation function thatforces a shape of a profile of each process step to be similar to anactual physical shape is used in the activation function layer 232 fHowever, the activation function may be in the shape of a functionindicating an etch amount accumulated from the first process step to thecurrent process step.

The custom layer 234 f receives the output Yk−1 of the previous processstep and the process result Y*k of the current process step. The customlayer 234 f is an added layer to directly calculate a causalrelationship in which a process result of a current process step isaccumulated on the output Yk−1 corresponding to a process result of aprevious process step. For example, in the case of the etching process,the custom layer 234 f derives a physical law that an etching amount ofa current process step is added to the output Yk−1 corresponding to aprocess result of a previous process step. This common-sense physicallaw may be applied by the custom layers 234 f added to process emulationcells of all process steps.

FIG. 13 illustrates a configuration of a process emulation cellaccording to an example embodiment. Referring to FIG. 13 , a processemulation cell 200 g provides a structure based on a combination of theexample embodiment of FIG. 6 , the example embodiment of FIG. 8A, andthe example embodiment of FIG. 9 .

An enhanced loss function (L_(k)) 232 g is applied to a profile network210 g. The profile network 210 g of an example embodiment may receivethe output Yk−1 of the previous process step, the common input Xcom, andthe sequence recipe Xk of the current process step. The training orestimation operation may be performed by using the input values. Inparticular, the enhanced loss function (L_(k)) 232 g is used in theprofile network 210 g. The training may be performed to suppress thegeneration of a profile of a shape that cannot appear in a realapplication by using the enhanced loss function (L_(k)) 232 g. Inparticular, the profile network 210 g may be provided with the priorinformation Y′k from simulation software such as the TCAD 300. Theprofile network 210 g generates and trains the critical parameters (λ,z) to be provided to an activation function layer 234 g by using theprior information Y′k.

The activation function layer 234 g is provided with the trained orestimated critical parameters (λ, z) from the profile network 210 g. Inaddition, the activation function layer 234 g is provided with thephysical parameter “x” of the current process step. The physicalparameter “x” may mean, for example, a variable indicating a size of adepth or a width in an etching process. An activation function thatforces a shape of a profile of each process step to be similar to anactual physical shape is used in the activation function layer 234 g.However, the activation function may be in the shape of a functionindicating an etch amount accumulated from the first process step to thecurrent process step. In this case, an output of the activation functionlayer 234 g may be the output Yk indicating a profile accumulated untilthe current process step. In the above condition, the profile network210 g may be trained to generate the critical parameters (λ, z) forgenerating the accumulated output Yk or the output Y*k of the currentprocess step in the same form as the activation function.

FIG. 14 is a diagram illustrating a configuration and a feature of aprocess emulation cell according to another example embodiment.Referring to FIG. 14 , a process emulation cell 200 h provides astructure based on a combination of the example embodiment of FIG. 7 ,the example embodiment of FIG. 8A, and the example embodiment of FIG. 9. The process emulation cell 200 h may include the TCAD 300 providingthe prior information Y′k, a profile network 210 h, and a custom layer230 h.

The profile network 210 h may receive the output Yk−1 of the previousprocess step, the common input Xcom, and the sequence recipe Xk of thecurrent process step. The training or estimation operation may beperformed by using the input values. In particular, the profile network210 h may generate the output Y*k of the current process step indicatinga process amount of only the current process step by using the outputYk−1 of the previous process step, the common input Xcom, and thesequence recipe Xk of the current process step. In particular, theprofile network 210 h may be provided with the prior information Y′kfrom simulation software such as the TCAD 300. The profile network 210 hgenerates and trains the output Y*k of the current process step to beprovided to the custom layer 230 h by using the prior information Y′k.

The custom layer 230 h receives the output Yk−1 of the previous processstep and the process result Y*k of the current process step. The customlayer 230 h is an added layer to directly calculate a causalrelationship in which a process result of a current process step isaccumulated on the output Yk−1 corresponding to a process result of aprevious process step. For example, in the case of the etching process,the custom layer 230 h derives a physical law that an etching amount ofa current process step is added to the output Yk−1 corresponding to aprocess result of a previous process step. This common-sense physicallaw may be applied by the custom layers 230 h added to process emulationcells of all process steps.

In a process simulation device and a method thereof, according toexample embodiments, a profile of each step of a process may beestimated with high accuracy. In addition, according to the processsimulation device of an example embodiment, a profile may be estimatedfor each process step with high accuracy only by using data of a finalprofile. Accordingly, a time and costs required for designing asemiconductor process to perform process tuning may be substantiallyreduced. Additionally, based on the estimated profile for each processstep, a defect inspection operation of detecting a defect in a processstep may be performed with high accuracy.

Although a few embodiments of the disclosure have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from theprinciples and spirit of the disclosure, the scope of which is definedin the claims and their equivalents.

What is claimed is:
 1. A process simulation system comprising: a randomaccess memory (RAM) to which a process simulator is loaded, the processsimulator comprising a plurality of process emulation cells operating asa time series-based recurrent neural network (RNN) and outputtingprofiles respectively corresponding to steps of process of manufacturingsemiconductors; and a central processing unit (CPU) configured tocontrol the plurality of process emulation cells to train and predictthe profiles based on a final target profile corresponding to a finalstep among the steps of process, wherein a first process emulation cellamong the plurality of process emulation cells is configured to, basedon a previous output profile that is output at a previous process stepin time series, a target profile of a current process step, processcondition information and prior knowledge information, generate acurrent output profile corresponding to the current process step,wherein the process condition information indicates one or moreconditions to be applied in the current process step, and wherein theprior knowledge information is used to filter the current output profileor limit a shape indicated by the current output profile.
 2. The processsimulation system of claim 1, wherein the first process emulation cellincludes: a profile network configured to, based on the previous outputprofile, the target profile and the process condition information,generate the current output profile; and a prior knowledge networkconfigured to provide the prior knowledge information to the profilenetwork.
 3. The process simulation system of claim 2, wherein theprofile network is configured to generate the current output profile byusing an enhanced loss function provided from the prior knowledgenetwork.
 4. The process simulation system of claim 3, wherein theprocess of manufacturing the semiconductors includes an etching process,a deposition process, an ion implantation process and a diffusionprocess, and wherein the enhanced loss function includes at least oneof: a first equation applied to the etching process, the depositionprocess and the ion implantation process; and a second equation appliedto the diffusion process.
 5. The process simulation system of claim 4,wherein the first equation includes a “Relu” function of a valueobtained by subtracting an output value corresponding to the currentoutput profile from an output value corresponding to the previous outputprofile.
 6. The process simulation system of claim 3, wherein theprofile network is configured to generate to the current output profileby further using separate information provided from a simulationprogram.
 7. The process simulation system of claim 1, wherein the firstprocess emulation cell includes: a profile network configured to, basedon the previous output profile, the target profile and the processcondition information, generate a process result in the current processstep; and a custom layer configured to, based on the previous outputprofile and the process result, generate the current output profile. 8.The process simulation system of claim 7, wherein the custom layer isconfigured to, based on the prior knowledge information, generate thecurrent output profile by adding the previous output profile and theprocess result.
 9. The process simulation system of claim 7, wherein theprocess of manufacturing the semiconductors includes an etching process,a deposition process, an ion implantation process and a diffusionprocess, and wherein the custom layer is applied to one of the etchingprocess, the deposition process and the ion implantation process. 10.The process simulation system of claim 1, wherein the first processemulation cell includes: a profile network configured to, based on theprevious output profile, the target profile and the process conditioninformation, generate critical parameters; and an activation functionlayer configured to, based on the critical parameters and physicalparameters in the current process step, determine an activationfunction, and configured to, based on the activation function, generatethe current output profile.
 11. The process simulation system of claim10, wherein, when the process of manufacturing the semiconductors is anetching process, the activation function has a form of an exponentialfunction.
 12. The process simulation system of claim 10, wherein theprofile network is configured to generate the critical parameters usingan enhanced loss function.
 13. The process simulation system of claim12, wherein the profile network is configured to generate the currentoutput profile by further using separate information provided from asimulation program.
 14. The process simulation system of claim 1,wherein the first process emulation cell includes: a profile networkconfigured to, based on the previous output profile, the target profileand the process condition information, generate critical parameters; anactivation function layer configured to, based on the criticalparameters and physical parameters in the current process step,determine an activation function, and configured to, based on theactivation function, to generate a process result in the current processstep; and a custom layer configured to, based on the prior knowledgeinformation, generate the current output profile by adding the previousoutput profile and the process result.
 15. The process simulation systemof claim 14, wherein the profile network is configured to generate theprocess result by further using separate information provided from asimulation program.
 16. A process simulation system comprising: a randomaccess memory (RAM) to which a process simulator is loaded, the processsimulator comprising a plurality of process emulation cells operating asa time series-based recurrent neural network (RNN) and outputtingprofiles respectively corresponding to steps of process of manufacturingsemiconductors; and a central processing unit (CPU) configured tocontrol the plurality of process emulation cells to train and predictthe profiles based on a final target profile corresponding to a finalstep among the steps of process, wherein the profiles are generatedbased on a training, which is performed based on prior knowledgeinformation defining a time series causal relationship in the process ofmanufacturing the semiconductor.
 17. The process simulation system ofclaim 16, wherein the prior knowledge information is provided in a formof at least one of an activation function, a loss function and a customlayer of the RNN.